Channel Decoding with a Bayesian Equalizer

In this paper we show that, in case of uncertainties during the estimation, overconfident posterior probabilities tend to mislead the performance of soft-decoders. Maximum likelihood (ML) estimates of the channel state information (CSI) make the equalizer to provide overconfident posterior probabilities of the equalized symbols half of the time, that can derail the decoder in case of wrong estimated bits. Thus, as a solution we propose and analyze a Bayesian equalizer that produces more accurate probabilities, because it considers the uncertainties in the estimation. This approach is based on an averaged BCJR over the probability density function of the estimated CSI. We exploit the improvement in the posterior probabilities by feeding the channel decoder with these better estimates. The proposed method exhibits a much better performance compared to the ML-BCJR when a LDPC decoder is considered, as illustrated in the experiments.

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Luis Salamanca, Juan José Murillo-Fuentes, Fernando Pérez-Cruz
• Keywords: CORR 2010, Education, Overconfident Posterior Probabilities, Posterior Probabilities, Wrong Estimated Bits